In modern days, owing to massive online competition, retail store owners are increasingly interested in the ability to, in the most effective manner, understand the browsing behavior and intentions of consumers inside the physical stores. During a visit to the physical store, such consumers may move around and may (or mayn't) interact with the one or more products. For example, in one scenario, a consumer may interact with multiple products but purchase a very few products from the multiple products. In another scenario, another consumer may spend a significant amount of time in the physical store but may not purchase any product at all. Therefore, it may be necessary for an individual, such as an owner (or an administrator) of the physical store or a service provider, to know about behavioral characteristics of the consumers when they are moving inside the shopping store or interacting with the one or more products in the shopping store.
Currently, various devices, such as aisle-level location tracking device, RFID based asset monitoring device, and/or smart glass-based browsing monitoring device, may facilitate to capture such individual and collective in-store behavior of the customers. However, such devices may require high cost infrastructure support. Further, data captured by such devices may not be useful and efficient enough to determine the behavioral characteristics of the consumers in the physical stores. Therefore, a much more effective and efficient system may be required to determine the behavioral characteristics of the consumers in the physical stores.
Further, limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.